In numerous experiments in the field of modern biomedical research, fluorescence techniques, amongst others, are used for marking in order to make biologically significant objects visible under the microscope. Such objects may be cells of a certain type or of a certain state, for example. In recent years, the progress made in automating such experiments has made it possible for biomedical laboratories to perform large numbers of such image-producing experiments fully automatically.
DE 197 09 348 C1, for example, describes a fluorescence microscope technique which produces a set of n images of a single sample by preparing said lymphocyte sample with n different fluorochrome markers. In each image, different lymphocyte subsets will be fluorescent and appear to have shining outlines. Each lymphocyte in the sample will have its specific fluorescent behavior in the image set. It will appear fluorescent in one subset of images, otherwise remaining invisible in the other images of the set.
For extracting the fluorescence patterns from the image set, the fluorescent lymphocytes will first of all have to be detected in the n images. The fluorochrome marked lymphocytes differ in number, location and intensity. Since such vast numbers of images and image data will result, from which the information will first of all have to be extracted for biological interpretation later on, there will be a so-called bottleneck in the evaluation of the experiments. Image interpretation by human employees is impractical since it is too time-consuming and the results are often not reliable. This is due to the visual evaluation work which is tiring and will lead to concentration losses already after a short time only. Moreover, the objects to be detected differ in number, location and intensity. Consequently, image parameters such as contrast and noise will differ from image to image. Furthermore, the objects, for example cells in tissue samples, vary considerably as regards their shape and size.
Consequently, there is a need for automatic evaluation methods which are capable of locating the objects to be detected in one image.
Earlier work on the automation of cell detection essentially focused on model-based approaches. Numbering amongst these is also the idea to adapt a geometric model to a gradient ensemble (Mardia et al., 1997, In: IEEE Transactions of Pattern Analysis and Machine Intelligence, 19: 1035-1042). This also includes the exploitation of wave propagation (Hanahara and Hiyane, 1990, In: Machine Visions and Applications, 3: 97-111) or a Hough transformation for detecting circular objects (Gerig and Klein, 1986, In: Proc. Int. Conf. on Pattern Recognition, 8: 498-500). These approaches, however, have the disadvantage that they are frequently susceptible to changes in the shape of the object, and they may not be readily adapted by persons not skilled in the art. Furthermore, the images will not infrequently contain noise, owing to heterogeneous lighting conditions, and the cells will be partly obscure, which makes detection by boundary scanning unsuitable (Galbraith et al.; 1991, In: Cytometry, 12: 579-596).